Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

II. Algorithms and Architectures

In this part, we cover algorithms and architectures. Specifically, we cover some of the basics to give you a feel for some of the elements of data infrastructure. And then we describe a basic process happening in this environment: model training.

Chapter 8, “Comparison,” covers a useful collection of metrics and ways to compute them: similarity metrics. These can be especially useful for simple content recommendations systems but are useful to have in your toolbox whatever your domain.

Chapter 9, “Regression,” describes supervised machine learning from the point of view of regression. The focus is less on the estimation error and more on the prediction and training problems. The chapter starts with linear regression ...